Method and apparatus for multi-user detection using RSFQ successive interference cancellation in CDMA wireless systems

ABSTRACT

A method and apparatus for using a multi-user detector for reducing multiple access interference (MAI) in a direct sequence CDMA wireless system such as W-CDMA. A superconducting rapid single flux quantum (RSFQ) RF digital receiver operating in combination with an RSFQ successive interference canceller (SIC) is located in the base stations of the wireless system. In the present invention, the RSFQ SIC is a vector machine capable of processing the cross-correlation matrices using an iterative method to decorrelate the user binary code sequences from the input signal in which the interference components are removed. According, the reduction in interference results in a significant increase in system capacity while improving cellular coverage area.

FIELD OF THE INVENTION

The present invention relates generally to interference reduction inwireless telecommunication systems and, more particularly, tointerference cancellation for reducing interference in CDMA basedsystems.

BACKGROUND OF THE INVENTION

Wireless telecommunication service providers are keenly interested inproviding high quality, reliable services for their customers in today'shighly competitive marketplace. A significant aspect affecting theservice quality is the consistency of radio coverage within cellcoverage areas of the network. Moreover, an additional aim from theprovider's perspective is to be able to increase capacity whilemaintaining quality and reliability. As known by those skilled in theart, telecommunication networks operating in accordance with directsequence code division multiple access (DS-CDMA), which are alsoreferred to as spread spectrum systems, the service quality isparticularly affected by the number of users in the cell. This isbecause the inherent nature of spread spectrum systems permits all usersto transmit and receive on a common frequency band thus each of thetransmissions necessarily “interfere” with each other in what is knownas multiple access interference (MAI).

As the number of users in the cell increase more interference isintroduced causing the mobiles to transmit with increased power in orderto sufficiently communicate with the base station and thereby making theproblem worse. This effect tends to be more prominent on uplinktransmissions from mobiles since their power levels tend to be morelimited in comparison to that of the base station. Another consequenceof increased interference is that the cell coverage area tends tocontract, on the other hand as less traffic is present, the coveragearea of the cell tends to expand. The tendency for cells to shrink andexpand in relation to number of users in the cell is known as “cellbreathing” and occurs, for example, since each user in a CDMA systemcumulatively contributes to the interference in the cell since theysimultaneously share a common frequency band.

Another type of interference that has a significant affect on CDMAsystems is multipath interference. Radio channel signals between atransmitter and receiver typically do not propagate only along one path.Reflections and refractions of a signal, which are particularly acute inurban environments having many buildings and obstructions, will bereceived over a number of different paths which are copies of thetransmitted signal, each having different amplitudes, phases, temporaldelays and arrival angles. At the receiver these signals can interferewith each other, in some instances being constructive at some points anddestructive at others. Still another type of interference is theso-called near-far interference which happens when a strong signal froma mobile close to the base station overwhelms a weaker signal from adistant mobile closer to the cell boundary, for example.

An advantage of CDMA systems is that spread spectrum modulation combinedwith the use of a Rake receiver, can be effective against multipathinterference. Rake receivers are used to receive and resolve multipathsignals in which multiple copies of the signal are received with varyingdelays. This is because the spread spectrum waveform is well matched tothe multipath channel and thus CDMA signals employ the use of multipathdiversity, which also reduces the effect from signal fading.

FIG. 1 shows a simplified block diagram of an exemplary direct sequenceCDMA transmitter. The binary data signal is directly modulated by adiscrete code valued signal that is discrete in time. The data signal ismultiplied by the code signal shown by the code generator block 110whereby the resulting signal modulates the wideband carrier shown by thewideband modulator block 120. A carrier generator 130 then modulates thewideband carrier signal 120 for transmission through antenna 140.Various code modulation techniques can be used. Often these are a formof phase shift keying such as binary phase shift keying (BPSK) orquadrature phase shift keying (QPSK), for example. The code signal isreferred to as the chip rate in which one chip is equal to one symbol inthe spreading code signal.

FIG. 2 shows a simplified block diagram of an exemplary direct sequenceCDMA receiver. At the receiver, the signal is first down converted withthe help of carrier generator 250. The signal is then coherentlydetected and filtered with the help of filter matching on the chipwaveform that is then despread with despreader block 210. In order todespread the signals, the receiver must know the code sequence used inspreading the signal, the codes of the received signal, together withthe condition that the locally generated code generated by codegenerator 220 must be synchronized. The code synchronization trackingblock 230 performs the operation of synchronizing the signal during theentire time the signal is being received. The signal despread bydespreading block 210 is fed into data demodulator block 240. The datademodulator block 240 demodulates the signal in order to allow theoriginal data to be recovered. Recovery of the data is possible at thereceiving end since the code sequences are known a priori.

The lack of spectral resources coupled with increasing demand forwireless voice and data services such as Internet access, audio, video,and multimedia applications have highlighted capacity as a criticalissue. Among other things the lack of capacity has been the drivingforce behind the shift to wideband systems such as Wideband CodeDivision Multiple Access (W-CDMA). W-CDMA standard is a preferred airinterface fulfilling 3G requirements for improved quality of service andcapacity. It is able to provide connections of 384 bits/s for mobileapplications and as much as 2 Mbits/s in stationary environments. Thecapacity of direct sequence DS-CDMA systems such as W-CDMA using a Rakereceiver is interference limited i.e. more users in a cell creates moreinterference for all the other users. Since the spreading codes insignals from the users are not completely orthogonal, this results inresidual interference within the cell, known as multiple accessinterference (MAI), where when together with interference fromneighboring cells, significantly degrades performance in the cell. MAIis a major factor in limiting cell capacity and the removal of suchinterference would lead to a significant increase in capacity.

A conventional approach for dealing with interference is by employing asingle-user matched filter in combination with Rake combiner. The usersuse spreading sequences of nearly uncorrelated codes so thatinterference from other users are treated as non-coherent interferenceand rejected, however this technique has been shown not to be theoptimal approach. This is because the sum of the cross-correlationbetween codes at high loading can be significantly larger than theautocorrelation that is detected. Furthermore, the interference itselfcontains much information on the structure and content of the signal andcan be used advantageously. In W-CDMA, multi-user detectors (MUD), alsoreferred to as interference cancellers, provide a means for reducing theeffect of multiple access interference. A multi-user detector (MUD) isan advanced detector in base stations that uses a more sophisticatedapproach to remove interference components from the signal. The benefitof using MUDs is that they dramatically increase system capacity.Furthermore, they can be used effectively for mitigating the effect ofnear-far interference that typically can plague DS-CDMA systems, byfirst detecting, and then subtracting the problem mobile from the inputsignal.

However, use of the optimum multi-user detector has not been found to bepractical for implementation. This is because the complexity of theoptimum detector becomes exponential to the number of users and requirescomputations that are too demanding for the current silicon based ICprocessing technology or any conventional digital technology currentlyemployed. Thus, a number of suboptimum multi-user and interferencereceivers have been proposed or developed. The suboptimum receivers canbe classified into two major categories: linear multi-user detectors andsubtractive interference cancellers. Linear detectors apply a lineartransform into the outputs of the matched filters that try to remove theMAI i.e. interference resulting from correlations between user codes.Examples of linear multi-user detectors that are most commonly referredto are decorrelating detectors, where the linear filter has a zerooutput, and the minimum mean square error (LMMSE) detector, where thelinear filter has a minimum output energy. In subtractive interferencecancellation, the MAI is estimated and then subtracted from the receivedsignal. The cancellation can be performed with successive interferencecancellation (SIC) or with parallel interference cancellation (PIC).

The successive cancellation technique requires at least severaliterations for a user however the individual iterations are less complexthan with the parallel cancellation technique. Furthermore, SIC does notdistinguish users from one another from the spreading sequences and thecanceling is performed serially in which the delay bits are added suchthat the complexity increases linearly with the number of users anditerations. Moreover, SIC has less computational complexity than PIC,which is more hardware intensive to process users in parallel. In acomparative sense, PIC is based on a Jacobi algorithm and thus requiresspecific conditions on the interference matrix for convergence. Specialtechniques can be used to reach convergence for particular scenario ofmobile transmission but there is generally no unique solution. Moreover,PIC typically requires more iterations than SIC, which means theconverge rate is usually much slower. The computational complexity isconsiderably larger in PIC due to the intensive parallel processing thathas to be applied. In SIC, the converge rate is generally much fasterand the processing flow can be serialized.

Even though the relative complexity is lower, the computational demandsfrom SIC require processor circuits capable of producing several Gigaoperations per second, which is extremely challenging when usingconventional integrated circuits. The serial nature of the SIC algorithmrequires very fast electronics in order to process the many channelspresent in the multiple access interference. Furthermore, when assuminglong spreading codes, the cross-correlation between the spreading codeschanges from symbol to symbol instead of over several symbols, therebyincreasing the complexity and required processing even further. In fact,the reason for the option of using short spreading codes in W-CDMA wasto enable the future use of a multi-user detector with the projectedavailable processing power. However, the use of long codes is preferabledue to better interference averaging, and thus better performance incanceling the interference. The implementation of comprehensive SICalgorithms are currently not practical due to conventional hardwarelimitations and, at present, only partial cancellation methods have beenconsidered, which have not been found to be very beneficial.

In view of the foregoing, it is desirable to provide a technicallyviable solution for using interference cancellation in wireless CDMAsystems.

SUMMARY OF THE INVENTION

Briefly described and in accordance with embodiments and featuresthereof, the invention provides a method and apparatus for implementinga multi-user detector for reducing multiple access interference (MAI) indirect sequence CDMA wireless systems such as W-CDMA. In accordance witha first architecture embodiment of the present invention, adecorrelator-Rake arrangement is implemented where an input signal,received by the base station, comprising a plurality user of partiallycross-correlated binary code sequences is fed into a bank of matchedfilters (despreaders) as a first stage of interference removal. Theoutput vector from the bank of matched filters is fed into asuperconducting rapid single flux quantum (RSFQ) vector processing unitapplying a successive interference cancellation (SIC) Gauss-Seideliterative algorithm to the cross-correlation matrices to decorrelate theuser binary code sequences. The interference components are removed andthe output from the RSFQ SIC is fed into a bank of Rake combiners torecover original data transmitted by the mobile users.

In a second architecture embodiment of the invention, aRake-decorrelator arrangement is implemented where the input signalcomprising a plurality of user binary code sequences is fed into a bankof matched filters. The output vector from the matched filters is fedinto a bank of Rake combiners. The output vector from the Rake combinersis fed into the RSFQ vector processing unit which applies a SICGauss-Seidel iterative algorithm to the cross-correlation matrices todecorrelate the user binary code sequences. The interference componentsare then removed to recover original data transmitted by the mobileusers.

In another aspect of the invention, within the RSFQ SIC the inneriterations, performed inside the main iterations dealing with signalsarrived at different time intervals, can be also performed in a parallelway using an Jacobi algorithm, for example.

In further aspect of the invention, the signal processing required formulti-user detection is performed before the matched filters. Furtherincluded is a memory for the storing signal processing data such thatthe size of the memory is in proportion to the total number oftransmitted signals per packet multiplied by the oversampling factor.

The invention, through which the reduction in interference is achieved,results in at least a two-fold increase in uplink capacity of the W-CDMAsystem while improving cellular coverage. Furthermore, the multi-userdetector of the invention improves power management and leads to thelowering of radiated power of approximately 4-10 dB while increasing theaverage data transmission rate with less retransmissions due to biterrors. Additionally, the multi-user detector can be installed into basestations as needed since it can be readily connected to preexistingoutputs of the matched filters or Rake combiners. The advantages will bemore clearly understood when taken together with the following detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further objectives and advantages thereof,may best be understood by reference to the following description takenin conjunction with the accompanying drawings in which:

FIG. 1 shows a block diagram of an exemplary direct sequence CDMAtransmitter;

FIG. 2 shows a block diagram of an exemplary direct sequence CDMAreceiver;

FIG. 3 shows a block diagram for a first embodiment architecture of theRSFQ SIC implemented into the digital signal processing chain of a basestation receiver operating in accordance with the invention;

FIG. 4 shows a block diagram for a second embodiment architecture of theRSFQ SIC implementation operating in accordance with the invention;

FIG. 5 shows a block diagram of a two-channel RSFQ cross-correlationcombiner operating in accordance with the invention;

FIG. 6 shows a simplified block diagram of an exemplary RFSQ MACoperating in accordance with the invention; and

FIG. 7 is a general block diagram of the RSFQ SIC multiprocessorarchitecture operating in accordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed toward improving the capacity andspectral efficiency in direct sequence CDMA systems, such as W-CDMA, byreducing the multiple access interference (MAI) generated by userswithin the cell and by neighboring cells. The presence of MAI can beattributed to the non-perfect separation of the users in the despreadingprocess. A practical implementation of a multi-user detector (MUD) inwireless base stations is carried out by overcoming the processingdeficiencies of prior art MUDs described in the preceding sections. Inaccordance with the present invention, the computational processing ofthe multi-user detector using long spreading codes is performed bysuperconducting digital rapid single flux quantum (RSFQ) devices.Further described by the present invention is the general idea ofmulti-user detection and various embodiment architectures that areimplemented in RSFQ technology.

The field of superconductor electronics has made remarkable advancesover the last 25 years. The commercial viability of superconductortechnology has been demonstrated by the success and use in products suchas MRI medical scanning machines for many years. As known by thoseskilled in the art, digital electronics based on rapid single fluxquantum (RSFQ) has been shown to provide extraordinary performancecharacteristics of ultra-high speed and extremely low power consumption.The storage information in RSFQ devices is based on the fundamentalphenomenon of quantization of magnetic flux and implemented in Josephsonjunctions. Ultra-high speed RSFQ logic circuits have been developed thatcan operate in excess of 100 GHz. RSFQ ICs can be fabricated fromseveral superconducting metals such as niobium (Nb/NbN) operating ataround 4-10 K (degrees Kelvin) or many of the so-called high-temperaturesuperconductors (HTS) operating between 20-77 K. Ultra-high speedsuperconductor logic circuits of this type are available e.g. fromHYPRES Incorporated of Elmsford, N.Y., U.S.A.

While these temperatures are extremely low, there are refrigerators,also referred known as cryocoolers, presently on the market that cansustain these temperatures that are very reliable and economical withservice lifetimes on the order of 15 years. Cryocoolers exist that havebeen developed specifically for use in wireless base stations that fitinto a standard 19-inch (28 cm) rack. Cryocoolers of this type are soldby e.g. Conductus Incorporated of Sunnyvale, Calif., U.S.A. andSuperconductor Technologies Incorporated of Santa Barbara, Calif.,U.S.A.

A description follows of two exemplary architecture embodiments used inthe implementation of the multi-user detector of the present invention.

FIG. 3 shows a block diagram for a first embodiment architecture of theRSFQ SIC implemented into the digital signal processing chain of aW-CDMA base station receiver operating in accordance with the invention.The RSFQ SIC operates with data coming either from a despreader ormatched filters 310. Inserted in this way into the signal processingchain the RSFQ SIC becomes an add-on component that does not requiremodifications to the other electronics.

FIG. 4 shows a block diagram for a second embodiment architecture of theRSFQ SIC implementation operating in accordance with the invention.Similarly, with RSFQ SIC operates with data coming after the Rakecombiner 410 thereby requiring no other modifications to theelectronics. The RSFQ SIC has three inputs: vector of despreadedsymbols, vector of channels gains, and vector of relative channelsdelays available at the Channel Estimation Unit 300 (CEU). The basestation receiver described supports coherent reception of the channel, afront-end digitizer with minimum required resolution 5 bits and 4 timesoversampling over the chip rate 3.84 mcps, amplitude estimation for thechannels, and the relative delay estimation for the channels.

With regard to the data channel, due to the successive method ofinterference rejection there is a trade off between system load, datarate and number of iterations required to reach target performance. Theload of the system is defined as a product of number of active DedicatedPhysical Channel (DPCH) and number of effective multi-paths as describedearlier. Supporting SNR gain in 10 dB at BER equal to 0.001, the RSFQSIC processes at least 100 DPCH with 10 effective multi-paths atpre-spreading data rate 2 mbps. In the data channel, the data is spreadwith variable spreading factor and the channel data may be scrambled byeither long or short scrambling code based on families of the Goldcodes, m-sequences, or their combinational fellows.

Regarding the multi-path propagation conditions considered for operationof the RSFQ SIC are multi-path fading and an Additive White GaussianNoise (AWGN) environment having a maximum relative delay 0.25 μs.Moreover, slow fading conditions and constant multi-path power duringone symbol interval is assumed. Also assumed is a Rayleigh amplitudedistribution and equal probable relative phase shift, that givesGaussian power distribution with zero mean.

System Model and Detection Algorithms

Operation of the RSFQ SIC is based on block-wise interferencecancellation using the Gauss-Seidel iterative method. Upon eachiteration a vector of symbol estimates at time T is updated using theobtained on the previous iteration information about the vector ofsymbols transmitted at time T−1 and T+1. Number of iterationscorresponds to the number of time symbol intervals involved intoconsideration. The RSFQ SIC of the invention supports up to 7iterations.

The standard vector model used in the two embodiment architectures ofthe multi-user detectors of the present invention is described. For theinterested reader the details of which can be found in the appendix atthe end of the description.

At the conventional part of the receiver, the incoming radio frequency(RF) signal is amplified, down-converted to baseband (complex signals),and filtered by a chip-matched filter. The output of the chip-matchedfilter is digitized at the rate of Ns f_(chip) samples per second, whereN_(S) is the oversampling factor and f_(chip) is the chip rate(f_(chip)=3.84 Mchip s⁻¹ in W-CDMA). The digital signal is forwarded tothe matched filter, where each of the K users are separated usingmatched filters tuned to the spreading codes of each individual user. Ifthe channel is frequency selective (several time-shifted replicas arereceived), we need one matched filter per user and signal path. Thesignal paths corresponding to one user are then weighted together in theRake receiver.

Since matched filter outputs are already available in existing systems,it is convenient to allow a multi-user detector to use these outputs.Hence, the multi-user detector becomes an add-on that can be added laterif improved performance is needed. Furthermore, it can be shown that thematched filter bank is actually an optimum pre-processing device in thesense that all necessary information needed to perform optimummulti-user detection is available.

The output of the bank of matched filters can be expressed in vectorform as (see appendix),y _(MF) =R _(MF) Hd+n  (1)where y_(MF) is the output of a bank of matched filters, R_(MF) is areal-valued cross-correlation matrix, H is a block diagonal matrix ofcomplex channel gains, d is a vector of the bits of all users (coded as±1), and n is a complex-valued noise vector.

The task of the receiver is to detect d given the observation y. Thiscan be done in many ways. We will here describe two possible variants ofthe so-called decorrelator multi-user detector. The two embodimentarchitectures are also illustrated in FIGS. 3 and 4. The iterativemethods that approximate the decorrelating detector are described in thesection on Successive Interference Cancellation.

Decorrelator-Rake Receiver

Regarding to the first embodiment architecture, we note that,y _(MF) =R _(MF)(Hd+R _(MF) ⁻¹ n)=R _(MF) u _(MF).

Hence, we can find u_(MF) by solving a system of linear equations. Thisreferred to as the decorrelating step, since it is equivalent topre-multiplying with the inverse of a correlation matrix,u _(MF) =R _(MF) ⁻¹ y _(MF) =Hd+R _(MF) ⁻¹ n.  (2)

We can form the decision for d by Rake-combining the output from thedecorrelator as,{circumflex over (d)}=sgn Re{H*u _(MF)}=sgn Re{H*Hd+H*R _(MF) ⁻¹n.}  (3)where the superscript * indicates the complex conjugate transpose andthe sign operation is taken element-wise. Since H*H can be shown to be adiagonal matrix with positive elements, we make correct decisions if thenoise vector is sufficiently small. The resulting architecture is shownin FIG. 3.

The elements of the H matrix are produced by the channel estimationunit. However, the RSFQ part of the receiver needs to compute R_(MF). Inthe appendix, it is shown that the (q, r) the element of R_(MF) can becomputed to be,

$\begin{matrix}{\left\lbrack R_{MF} \right\rbrack_{q,r} = {\sum\limits_{m = {- {({N - 1})}}}^{N - 1}{v\left( \left\lbrack {{mN}_{s} + {\left( {n - n^{\prime}} \right){NN}_{S}} + \left( {p_{k,l} - P_{k^{\prime},l^{\prime}}} \right) +} \right. \right.}}} \\{\left. {\left. {QN}_{s} \right\rbrack{T_{c}/N_{s}}} \right){\sum\limits_{i = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack i\rbrack}{c_{n^{\prime},k^{\prime}}^{\prime}\left\lbrack {i - m} \right\rbrack}}}}\end{matrix}$where k, l, n and k′, l′, n′ depend on q and r, c_(n,k) ^(′) [i] is thecode sequence for the nth user's kth symbol, v(t) is a causal pulse witha raised-cosine spectrum (assumed to have effective support [0,2QT_(c)]), T_(c)=l/f_(chip), and τ_(k,l)=p_(k,l)T_(c)/N_(s) is the delayof the kth user's lth propagation path. The samples of v(t) can bepre-computed and stored in a table, and the code sequences are generatedin the receiver front-end. We note that [R_(MF)]_(q,r) is a linearcombination of 2N−1 terms, where each term is a cross-correlationbetween the binary code sequences c_(n,k) ^(′) [i] and c_(n′,k′) ^(′)[i] at a certain lag.Rake-decorrelator Receiver

With regard to the second architecture embodiment, we can also startwith Rake-combining as,

$\begin{matrix}\begin{matrix}{y_{rake} = {H^{*}y_{MF}}} \\{= {{H^{*}R_{MF}{Hd}} + {H^{*}n}}} \\{= {{R_{rake}d} + {H^{*}n}}} \\{= {R_{rake}\left( {d + {R_{rake}^{- 1}H^{*}n}} \right)}} \\{= {R_{rake}u_{rake}}}\end{matrix} & (4)\end{matrix}$followed by a decorrelating step,u _(rake) =R _(rake) ⁻¹ y _(rake) =d+R _(rake) ⁻¹ H*n.  (5)

Again, we will make correct decisions if the noise vector issufficiently small. The resulting architecture is shown in FIG. 4.

Clearly, the receiver needs access to R_(rake). In the appendix, it isshown that the (q, r)th element of R_(rake) can be computed to be,

$\begin{matrix}{\left\lbrack R_{rake} \right\rbrack_{q,r} = {\sum\limits_{i = 1}^{L}{\sum\limits_{i^{\prime} = 1}^{L}{h_{k,l}^{*}{h_{k^{\prime},l^{\prime}}^{*}\lbrack A\rbrack}_{l,l^{\prime}}}}}} & (6)\end{matrix}$where A is a certain L×L submatrix of R_(MF), and h_(k,l) is the complexchannel gain of the kth user's lth propagation path. The definitions ofA, k and k′ depend on q and r.Successive Interference Cancellation

In the previous section we have presented two architectures formulti-user detection; the decorrelator-Rake and the Rake-decorrelatorillustrated in FIGS. 3 and 4 respectively. As stated previously, thedecorrelator solves a system of linear equations (equations (2) or (5)).These linear systems can be solved using iterative methods as, forexample, described in publication [4] by Rasmussen L K, Lim T J andJohansson A-L 2000, “A Matrix-Algebraic Approach To SuccessiveInterference Cancellation”, CDMA IEEE Trans. Commun. 48 145-51. The twomain categories are the SIC (Gauss-Seidel algorithm) and the ParallelInterference Canceller (PIC) (Jacobi algorithm). The SIC is, in general,more reliable in the sense that it is guaranteed to converge with anyinitial vector (if the cross-correlation matrix is positive definite,which is a reasonable assumption). Another advantage of the SIC is thefast rate of convergence. It has been shown that somewhere around seveniterations of SIC are sufficient to reach convergence. Afterconvergence, the resulting bit error rate is the same as for thedecorrelating receiver described previously.

To perform iterations on a symbol level two basic hardware blocks arerequired: the partial cross-correlation unit 320 (PCU) and the iterativelinear system solver 330 (ILSS). The PCU 320 calculates the elements inthe cross-correlation matrices R_(MF) or R_(rake). The outputs from thePCU 320 are used by the ILSS 330 to estimate either Hd or d depending onthe architecture used. In the case of the decorrelator-Rake (FIG. 3) theestimates of Hd are Rake-combined to form {circumflex over (d)} asdescribed in equation (3). Since the cross-correlation matrix is banded,the Gauss-Seidel algorithm can be modified to be a block-wise iterationon sub-blocks of the matrix. The width of the band is 3KL for R_(MF) and3K for R_(rake).

The computation and hardware complexity of the SIC depends highly on thesystem parameters. In accordance with the embodiment, a targetrealization of the SIC that takes into account realistic parameters ofthe commercially operational W-CDMA systems of 100 simultaneous voiceuser per sector i.e. K=100, at least 10 resolvable multipath componentsi.e. L=10, and the effective support of the chip waveform determined byQ=3. Under these considerations the throughputs of the PCUs for thedecorrelator-Rake and Rake-decorrelator (FIG. 4) receivers are 90 Gelements per second and 9 M elements per second, respectively, withcomputational complexity scaling as O(N(KL)²) and O(N(KL)²)+O((KL)²),and falling in the Teraflop range. The throughputs of the ILSS units arethen 30 Msymbol/s and 3 Msymbol/s for the same receivers withcomputational complexity scaling as O((KL)²) and 0(K²) respectively.

RSFQ Implementation Issues

Due to the Teraflop range of operations, the SIC would requireapplication specific integrated circuits (ASICs) for implementation ofboth parts, computing the elements of the cross-correlation matrix andperforming iterations.

The computation of each element of the cross-correlation matrices R_(MF)and R_(rake) consists of four parts:

-   -   generating all K L PN codes corresponding to all channel delays        plus (2Q+1)KL replicas shifted on ±QT_(c);    -   correlating all codes with each other;    -   weighting the correlations with the chip waveform values v[q] to        form R_(MF);    -   and further combining them using channel coefficients h_(k,l) to        form R_(rake).

The following is a discussion of the architectures of the PCU buildingblock, parallel-serial RSFQ cross-correlation combiner (CC), theoperation of the ILSS building block, and the parallel-serial RSFQmultiply-accumulate unit (MAC).

Cross-correlation Combiner

FIG. 5 shows a block diagram of a two-channel RSFQ cross-correlationcombiner operating in accordance with the invention. The CC calculateseach element of R_(MF) through three steps: generating the codes;computing cross-correlations; and computing their linear combinationsusing chip waveform values. The CC block has a regular structure and,for simplicity, two channels will be considered. Two 25-bit PNgenerators (500, 502) produce a maximum of 128 bit user specific codesc_(k,l) ¹, c_(k′,l′) ², using, as input from the channel estimation unit(CEU), two 25 bit delay masks m_(k,l) ¹ and m_(k′,l′) ²+Q, representingsymbol and chip offsets. The values of c_(k′,l′) ²[q] are taken from thebits of the second PN generator shifted from the last bit on 2Q.

The correlation measure of (c¹, c²[q]) is calculated as the number ofequal elements minus the number of unequal elements in the twosequences. Therefore, each pair of output bits from the PN generators iscompared on XOR and AND gates 510, then the results are forwarded to theripple counter 520. The counter requires 8 bits to be able to processthe maximum 128 is and to represent the result in two-th complementaryform.

To achieve partial cross-correlations, an internal clock generator isused that produces the number of clocks determined by the relativedelays between codes. All partial cross-correlations are multiplied onconstant supplied from CEU chip waveform gains v[q] (5 bit integernumbers) with the use of 2Q serial multipliers and accumulated with thehelp of 13 bit adders, one for each counter.

The code generation, computing cross-correlations, multiplication andaccumulation operations are pipelined. The critical stage of thepipeline is the generation and comparison of the maximum 128 bit codesthat should be partially parallelized to achieve the required speed of4.5 GHz. The serial PN generator based on the linear feedback shiftregister (LFSR) can be replaced by the sixteen identical generators.Each of these produces 8 bits of the 128 bit sequence. After 8 bits, thecontents of the shift register are loaded into the next generator, andthe previous one can be used for generation of the new code. The RSFQLFSR PN generator has a four-clock initialization cycle that makes thelatency of the whole construction with sixteen generators equal totwelve clocks. The interested reader may refer to the publication [11]by present inventor Kidiyarova-Shevchenko A. Yu 2002 RSFQ spreading codegenerator for multiuser detection Physica C, v. 368, pp. 222-226. Theoperation of the counters is parallelized in the same way, by replacingone 7 bit counter on 16 with the number of bits increasing from 3 to 7.When the first 8 bits have been processed in the first counter, thecontents are moved to the next.

In total, all the PN generators in the PCU consume about 22×10³Josephson junctions, with 340 Josephson junctions per generator (seeabove publication). The implementation of comparators and counters wouldrequire 17×10³ Josephson junctions. Each integer 5×7 multiplier andadder consists of about 400 Josephson junctions, which leads to 24×10³Josephson junctions in the PCU. Further discussion of which is given inthe publication [12] by Kidiyarova-Shevchenko A Yu 2002 RSFQ iterativelinear system solver for superconducting multiuser detection Physica C,v. 372-376, pp. 131-134. Targeting the integration density of 10,000Josephson junctions per chip, the implementation of the PCU would bepossible with a multichip-module (MCM) of about 63 chips. Such an amountof 5×5 mm² chips can be collected from one 6 inch wafer with a yield of20%.

ILSS Building Blocks

FIG. 6 shows a simplified block diagram of an exemplary RFSQ MAC(multiply accumulate unit) operating in accordance with the invention.As mentioned earlier, the complexity of the ILSS is determined bycomplex MACs performing the multiply/accumulate operation over thecross-correlation matrix row and element of the complex vector of thedata bit estimates. Since the real and complex components of the signalcan be processed independently, one complex MAC is a combination of twoidentical integer MACs. Each MAC consists of a shift register 610, 610′to store the elements of vector y_(MF), a serial multiplier 620, 620′ toperform the multiplication of vector y on matrix R_(MF) row, and aparallel adder 630, 630′ that accumulates the results of themultiplication and contents of the shift register. In accordance withthe Gauss-Seidel algorithm, each MAC uses elements of the vector Y_(MF)recently updated from the previous stages. Each MAC operatessimultaneously and the units communicate through a single bit bus.

The latency of the stage is the sum of the synchronization time of threeclocks and the delay of the serial multiplier, which in the conventionalcase is N+2, where N≦16 is the processing word length. A furtherdiscussion is given in the publication by Kidiyarova A. Yu. et al RSFQAsynchronous Serial Multiplier and Spreading Codes Generator forMultiuser Detector, IEEE Trans. Appl. Supercond. 2003 in press. The RSFQserial multiplier can be improved with the help of the ‘shifting overzeros’ technique that allows us to compute N×N bit products over−0.7(N+2) clock cycles and to use 30×N=390 Josephson junctions. Theexecution time of such a multiplier becomes data-dependent and requiresthe asynchronous operation of the ILSS. However, the asynchronousoperation is also natural for the implementation of the PCU where theamount of computations is dependent on overlapping between symbols.

Each stage of the ILSS unit processes both the complex and realcomponents of the data and comprises approximately 3000 Josephsonjunctions. In total, the realization of the ILSS would require a MCMwith about 20 chips operated at 54 GHz and with an integration densityof 10,000 Josephson junctions per chip. It should be noted that thespecifications provided are only approximate in which the specificationswill vary with the evolutionary state of the technology.

Architecture

FIG. 7 is a general block diagram of the RSFQ SIC multiprocessorarchitecture operating in accordance with the invention. The RSFQ SIC isa vector machine performing iterative solution of system of linearequations built on chains of processors consisting of an RSFQMultiply-accumulate unit (MAC) and an RSFQ Partial crosscorrelation unit(PCU). All processors 710 in a chain operate in parallel such that eachblock is computing one dedicated element of vector of transmittedsymbols at time T. In one processor, a PCU unit is used for generationof the interference coefficients corresponding to this element and a MACunit is used for interference cancellation at this element. Between theprocessors are dynamic memories, implemented as RSFQ shift registers720. The dynamic memory is used for loading the initial data from thedespreader or Rake combiner to store the intermediate estimates of thereceived symbols, in addition to communicating data between iterativestages.

Each MAC calls for its interference coefficients from the attached PCUstage. The PCU stage computes cross-correlation coefficients betweencodes using delay masks obtained from the CEU, combines thesecoefficients against the waveform amplitudes and combines the resultagainst the channel amplitudes. MAC stages produce symbol estimates, oneby one. Each obtained symbol estimate is fed to the next processorsuntil the end of iteration. The initial vector for the first chain atthe beginning of each computational cycle is the output of thedespreader of the Rake combiner.

Performance

Due to the successive nature of the algorithm the RSFQ SIC introducesdelay equal to the 3NTproc, where N is the dimension of the system andTproc, is a time required for execution of one PCU and MAC operation.Therefore, the throughput of the SIC is f_(data)=f_(proc)/3N. In thedecorrelator-Rake circuit both devices have similar delays coming fromthe serial spreading code generator and the serial multiplier. In theRake-decorrelator circuit PCU introduces the major delay sinceadditional serial multipliers are used for combining the multi-pathsagainst their amplitudes. As a result both approaches are comparable interms of throughput and hardware complexity.

Each processor operates with fixed-point arithmetic with maximum 16 bitsprecision. RSFQ implementation of PCU and MAC is based onparallel-serial architecture. Overall complexity will be dependent oncurrently available and future fabrication technology.

The present invention contemplates a general approach to the applicationof fast RSFQ technology into multi-user detection in 3G, W-CDMA,cellular systems. The utilization of fast speed of RSFQ circuits inorder to make multi-user detection simple and at the same time reliable,which is not practical with convention silicon-based technologies. Thespeed advantage of RSFQ allows computations in real time of all theelements of the cross-correlation matrix between codes to realize theSIC, which gives better performance and requires less hardware thanparallel algorithms. In the realization of RSFQ, a full-blown widebandCDMA system was considered with 100 voice equivalent channels where theparameters of which, have not fully been considered for multi-userdetection. Given the present state of RSFQ technology, implementation ofthe SIC is estimated to require a MCM with about 80 chips at anintegration density of 10,000 Josephson junctions per chip and anoperating frequency of approximately 54 GHz. As the technology advances,the operational speeds will increase accompanied with drasticallyreducing the number of chips thereby further lowering the cost ofimplementing the SIC. The resulting reduction in interference providedby the invention is especially suitable for transmitting data since thewireless transmission of data is much more sensitive to bit errors whencompared to voice transmissions.

Possible Modifications to Embodiments

A first possible modification can be that inside of main iterationsdealing with signals arrived at different time intervals, the so-calledouter iterations, the inner iterations can be done under some conditionsin parallel way. This is possible because the overall performance of theRSFQ SIC is not strictly dependent on how the inner iterations areperformed and thus a parallel algorithm such as a Jacobi algorithm canbe used. The change in the algorithm would require a change in thecombinations of processors and a more sophisticated mechanism inmanaging bus cash.

A second possible modification is of a more general nature in that thesignal processing required for multi-user detection can potentially bemoved before the matched filters. In this case, the bit stream from thefront-end ADC is fed directly into the RSFQ SIC. The RSFQ SIC has KLblocks where each block comprises a matched filter for dispreading thesignal, and a decision block and modulator to encode the detectedsignals back. After the encoding, the detected signals are subtractedfrom the total received signal. There are algorithms that deal with sucha structure. From the practical point of view, a large amount of memoryis needed because the incoming signal from the front-end signal shouldbe delayed by the time of execution of one operation by RSFQ SIC stage.The memory size would increase in proportion to the total number oftransmitted signals per packet multiplied by the oversampling factor.Various types of memory can be used such as static or dynamic memories(SRAM or DRAM), or flash memory (non-volatile memory). In the case ofstatic memory, a single block is placed before all stages of RSFQ SIC.The introduction of static memory increases the hardware complexity andreduces the speed due to the addressing, however advances in technologymay mitigate this aspect somewhat. In the case of dynamic memory, theRSFQ shift registers of appropriate length are placed between each ofthe stages.

Furthermore, the RSFQ SIC architecture can be based on the full-parallelarithmetic, e.g. full-parallel multipliers, adders, registers, and codegenerators. Full parallel architectures are difficult to implementoutside laboratory conditions due to the limited integration density inthe fabrication processes for reliable RSFQ devices. However, astechnology advances and densities increase this will be much morefeasible parallel architectures are possible. In general, to realizefull-parallel architecture an integration density of about 100000Josephson junctions per chip is required.

Although the invention has been described in some respects withreference to specified embodiments thereof, variations and modificationswill become apparent to those skilled in the art. In particular, theinvention is applicable to all direct sequence CDMA systems such asCDMA2000, for example, where the code correlator block is readilyadaptable to different standards and leaving the other blockssubstantially unchanged. Furthermore, the invention can be used, forexample, for interference rejection in a ship-to-land (and vice versa)communications link and in military communication systems. It istherefore the intention that the following claims not be given arestrictive interpretation but should be viewed to encompass variationsand modifications that are derived from the inventive subject matterdisclosed.

Appendix

Vector Model

Regarding the iterative methods for solving systems of linear equationsin the invention includes the need to form the R_(MF) or R_(rake)matrices, in which we show that this depends on the chip waveform andthe codes of the user, channel gains and delays.

For clarity, a simplified model of a full-blown W-CDMA system isdiscussed. However, the described architecture of the multi-userdetector can be easily adapted for a more complicated scenario.Specifically, a K-user system is considered where the baseband signalfor each kth user is,

${s_{k}(t)} = {\sum\limits_{n = 0}^{P - 1}{{d_{k}\lbrack n\rbrack}{c_{n,k}^{\prime}(t)}}}$where P is the number of transmitted bits per user (packet length) and,

${c_{n,k}^{\prime}(t)} = {\sum\limits_{j = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack j\rbrack}{{\psi\left( {t - {nT} - {jTc}} \right)}.}}}$

The signal ψ(t) is the chip waveform, f_(data)=1/T is the data rate(bits per second), and f_(clip)=1/T_(c)=N/T_(c) is the chip rate.

We assume that all the user signals are transmitted over L-path slowlyfading channels. Hence, the noise-free received baseband signal can bewritten as,

${z(t)} = {\sum\limits_{k = 1}^{K}{\sum\limits_{l = 1}^{L}{h_{k,l}{s_{k}\left( {t - \tau_{k,l}} \right)}}}}$where h_(k,l) and τ_(k,l) are the complex channel gain and delay of thekth user's lth path, respectively.

The received signal is fed through a linear filter with an impulseresponse ψ(−t) (i.e. chip-matched filtering), and the output of thefilter is sampled every T_(c)/N_(s) seconds (i.e. oversampling with afactor N_(s)) yielding the discrete time signal,

${r\left( {{iT}_{c}/N_{s}} \right)} = {{{{z(t)}^{*}{\psi\left( {- t} \right)}}❘_{t = {{iTc}/{Ns}}}} = {\sum\limits_{k = 1}^{K}{\sum\limits_{l = 1}^{L}{h_{k,l}{\sum\limits_{n = 0}^{P - 1}{{d_{k}\lbrack n\rbrack}{c_{n,k,l}\lbrack i\rbrack}}}}}}}$where * is the convolution operator. The effective spreading code is,

${c_{n,k,l}\lbrack i\rbrack} = {\sum\limits_{j = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack j\rbrack}{v\left( {\left\lbrack {i - {jN}_{s} - {nNN}_{s} - p_{k,l}} \right\rbrack{T_{c}/N_{s}}} \right)}}}$where v(t)=ψ(t)*ψ(−t) and τ_(k,l)=p_(k,l)T_(c)/N_(s). We assume thatv(t) is a pulse with a raised-cosine spectrum with roll-off a=0.22 (usedin W-CDMA) with the effective support t∈ [0, 2QT_(c)].

We form the vector r,r=[r(0) r(T _(c) /N _(s)) . . . r((M−1)T _(c) /N _(s))]^(T)where M is chosen to be large enough to capture the contribution fromall bits of all users. It is easy to show that,r=CHdwhere the columns of the M×KLP matrix C are samples of c_(n,k,l)[i]. Tobe precise, the (i, l+(k−1)K+nLK)th element of C is equal toc_(n,k,l)[i−1]. The KLP×KP matrix H is block-diagonal and defined as,H=diag(H ₀ , H ₁ , . . . , H _(P−1))H _(n)=diag(h ₁ , h ₂ , . . . , h _(K))h _(k) =[h _(k,l) h _(k,2) . . . h _(k,L)]^(T).

Finally,d=[d ₁[0] . . . d _(K)[0] . . . d ₁[1] . . . d _(K)[1] . . . d ₁ [P−1] .. . d _(K) [P−1]]^(T)

The output from the bank of the KL matched filters (one filter per userand path) can be found to be,y_(MF)=C_(MF) ^(T)rwhere C_(MF) is of the same dimensions as C. The (i, l+(k−1)K+nLK)thelement of C_(MF) is equal to C_(MF,n,k,l)[i−1], which is defined as,

${C_{{MF},n,k,l}\left\lbrack {{iN}_{s} + {nNN}_{s} + p_{k,l} + {QN}_{s}} \right\rbrack} = \left\{ \begin{matrix}{{c_{n,k}^{\prime}\lbrack i\rbrack},} & {{{{for}\mspace{14mu} i} = 0},1,\ldots\mspace{11mu},{N - 1}} \\{0,} & {otherwise}\end{matrix} \right.$

Hence, we have shown that,R_(MF)=C_(MF) ^(T)C

It can also be shown that, due to the finite support of v(t), the matrixC_(MF) is a band matrix with bandwidth 2KL. The (q, r)th element ofR_(MF) can be computed to be,

$\begin{matrix}{\left\lbrack R_{MF} \right\rbrack_{q,r} = {\sum\limits_{i = 0}^{N - 1}{{c_{{MF},n,k,l}\left\lbrack {{iN}_{s} + {nNN}_{s} + {QN}_{s} + p_{k,l}} \right\rbrack} \times}}} \\{c_{n^{\prime},k^{\prime},l^{\prime}}\left\lbrack {{iN}_{s} + {nNN}_{s} + {QN}_{s} + p_{k,l}} \right\rbrack} \\{= {\sum\limits_{i = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack i\rbrack}{\sum\limits_{j = 0}^{N - 1}{{c_{n^{\prime},k^{\prime}}^{\prime}\lbrack j\rbrack}{v\left( \left\lbrack {{\left( {i - j} \right)N_{s}} + {\left( {n - n^{\prime}} \right){NN}_{s}} +} \right. \right.}}}}}} \\\left. {\left. {\left( {p_{k,l} - p_{k^{\prime},l^{\prime}}} \right) + {QN}_{s}} \right\rbrack{T_{c}/N_{s}}} \right) \\{= {\sum\limits_{m = {- {({N - 1})}}}^{N - 1}{v\left( \left\lbrack {{mN}_{s} + {\left( {n - n^{\prime}} \right){NN}_{s}} + \left( {p_{k,l} - p_{k^{\prime},l^{\prime}}} \right) +} \right. \right.}}} \\{\left. {\left. {QN}_{s} \right\rbrack{T_{c}/N_{s}}} \right){\sum\limits_{i = 0}^{N - 1}{{c_{n,k}^{\prime}\lbrack i\rbrack}{c_{n^{\prime},k^{\prime}}^{\prime}\left\lbrack {i - m} \right\rbrack}}}}\end{matrix}$where k, l, n and k′, l′, n′ depend on q and r. Furthermore, an elementof R_(rake) is computed to be,[R _(rake)]_(q,r) =h* _(k) Ah _(k′)where A is a certain L×L submatrix of R_(MF). Again, the definitions ofA, k and k′ depend on q and r. We can rewrite the expression for[R_(rake)]_(q,r)

$\left\lbrack R_{rake} \right\rbrack_{q,r} = {\sum\limits_{l = 1}^{L}\;{\sum\limits_{l^{\prime} = 1}^{L}\;{h_{k,l}^{*}{h_{k^{\prime},l^{\prime}}^{*}\lbrack A\rbrack}_{l,l^{\prime}}}}}$

1. A method of reducing multi-user interference (MAI) in a wirelessdirect sequence CDMA wireless system comprising at least one basestation transmitting to and receiving signals from a plurality of mobilestation users, wherein the method includes reducing interference with amulti-user detector located within said at least one base stationcomprising the steps of: receiving at the at least one base station adigital input signal comprised of a plurality of binary code sequencesfrom the mobile station users; processing the digital input signal witha bank of matched filters and producing matched filter output vectorsfrom the bank of matched filters; feeding the matched filter outputvectors into a superconducting digital rapid single flux quantum (RSFQ)vector processing unit applying a successive interference cancellation(SIC) iterative technique (RSFQ SIC) to solve a cross-correlation matrixto decorrelate the plurality of binary code sequences; reducing theinterference by removing interference components between the binary codesequences; and feeding an output from the RSFQ SIC into a bank of Rakecombiners to recover original data transmitted by the mobile stationusers.
 2. A method according to claim 1 wherein the implementedmulti-user detector of the direct sequence CDMA wireless system operatesin accordance with the Wideband Code Division Multiple Access (W-CDMA)standard.
 3. A method according to claim 1 wherein in the receivingstep, the binary code sequences from the mobile station users are W-CDMAGold long spreading codes having a sequence length of 2⁴¹, or VL-Kasamishort spreading codes with a sequence length of
 256. 4. A methodaccording to claim 1 wherein the RSFQ SIC operates at a frequency of atleast 54 GHz and is capable of removing interference in a W-CDMA systemoperating with at least 100 simultaneous voice user per sector with atleast 10 resolvable multipath components per user, and an effectivesupport Q of a chip waveform determined by Q=3.
 5. A method according toclaim 1 wherein in the applying step, the SIC iterative techniqueemploys a Gauss-Seidel iteration algorithm to solve thecross-correlation matrix R_(MF) of the binary code sequences given by,$\left\lbrack R_{MF} \right\rbrack_{q,r} = {\sum\limits_{m = {- {({N - 1})}}}^{N - 1}\;{v\left( {\left\lbrack {{mN}_{s} + {\left( {n - n^{\prime}} \right){NN}_{S}} + \left( {p_{k,l} - P_{k^{\prime},l^{\prime}}} \right) + {\left. \quad{QN}_{s} \right\rbrack{T_{c}/N_{s}}}} \right){\sum\limits_{i = 0}^{N - 1}\;{{c_{n,k}^{\prime}\lbrack i\rbrack}{c_{n^{\prime},k^{\prime}}^{\prime}\left\lbrack {i - m} \right\rbrack}}}} \right.}}$where k, l, n and k′, l′, n′ depend on q and r, c_(n,k) ^(′) [i] is thebinary code sequence for the nth user's kth symbol, v(t) is a causalpulse with a raised-cosine spectrum, T_(c)=l/f_(chip), andτ_(k,l)=p_(k,l)T_(c)/N_(s) is a delay of the kth user's lth propagationpath, wherein q and r are the qth and rth element of R_(MF); T_(c) is asampling time with N_(s) being an oversampling factor.
 6. A methodaccording to claim 1 wherein a partial cross-correlation unit (PCU) isused to calculate the elements in the cross-correlation matrix R_(MF).7. A method according to claim 6 wherein the output from the PCU is usedby an iterative linear system solver (ILSS) to estimate a block diagonalmatrix Hd whereby a Gauss-Seidel algorithm is modified to be ablock-wise iteration on sub-blocks of the block diagonal matrix.
 8. Amethod according to claim 1 wherein the RSFQ vector processing unitreceives inputs that includes a vector of channels gains and a vector ofrelative channels delays from a channel estimation unit (CEU).
 9. Amethod of reducing multi-user interference (MAI) in a wireless directsequence CDMA wireless system comprising at least one base stationtransmitting to and receiving signals from a plurality of mobile stationusers, wherein the method includes reducing interference with amulti-user detector located within said at least one base stationcomprising the steps of: receiving at the at least one base station adigital input signal comprised of a plurality of binary code sequencesfrom the mobile station users; processing the digital input signal witha bank of matched filters to produce matched filter output vectors;receiving the matched filter output vectors into a bank of Rakecombiners to produce Rake combiner output vectors; receiving the Rakecombiner output vectors into a superconducting digital rapid single fluxquantum (RSFQ) vector processing unit applying a successive interferencecancellation (SIC) iterative technique (RSFQ SIC) to solve across-correlation matrix to decorrelate the plurality of binary codesequences; and reducing the interference by removing interferencecomponents between the plurality of binary code sequences.
 10. A methodaccording to claim 9 wherein the direct sequence CDMA wireless systemoperates in accordance with the Wideband Code Division Multiple Access(W-CDMA) standard.
 11. A method according to claim 9 wherein, in thereceiving step of the digital input signal, the binary code sequencesfrom the mobile station users are W-CDMA Gold long spreading codeshaving a sequence length of 2⁴¹, or VL-Kasami short spreading codes witha sequence length of
 256. 12. A method according to claim 9 wherein theRSFQ SIC operates at a frequency of at least 54 GHz and is capable ofremoving interference in a W-CDMA system operating with at least 100simultaneous voice user per sector with at least 10 resolvable multipathcomponents per user, and an effective support of a chip waveform.
 13. Amethod according to claim 9 wherein in the applying step, the SICiterative technique employs a Gauss-Seidel iteration algorithm to solvethe cross-correlation matrix R_(rake) of the binary code sequences givenby,$\left\lbrack R_{rake} \right\rbrack_{q,r} = {\sum\limits_{i = 1}^{L}\;{\sum\limits_{i^{\prime} = 1}^{L}\;{h_{k,l}^{*}{h_{k^{\prime},l^{\prime}}^{*}\lbrack A\rbrack}_{l,l^{\prime}}}}}$where A is a certain L×L submatrix of R_(MF), and h_(k,l) is a complexchannel gain of a kth user's lth propagation path, wherein q and r arethe qth and rth element of R_(MF); T_(c) is a sampling time with N_(s)being an oversampling factor.
 14. A method according to claim 9 whereina partial cross-correlation unit (PCU) is used to calculate the elementsin the cross-correlation matrix R_(rake).
 15. A method according toclaim 14 wherein the output from the PCU is used by an iterative linearsystem solver (ILSS) to estimate a block diagonal matrix d whereby aGauss-Seidel algorithm is modified to be a block-wise iteration onsub-blocks of the block diagonal matrix.
 16. A method according to claim9 wherein the RSFQ vector processing unit receives inputs that includesa vector of channels gains and a vector of relative channels delays froma channel estimation unit (CEU).
 17. A method according to claim 9wherein in the RSFQ SIC, inner iterations, performed inside of mainiterations dealing with signals arrived at different time intervals, areperformed in a parallel way.
 18. A method according to claim 9 whereinsignal processing required for said multi-user detector is performed ona chip level.
 19. A multi-user detector apparatus for use withinterference cancellation in a radio base station receiver operatingwithin a direct sequence CDMA wireless system, wherein said base stationreceiver includes a channel estimation unit (CEU) for estimatingcharacteristics of a channel, a pseudo-number generator for use withdespreading user code sequences, a bank of matched filters for rejectinginterference in an input signal, a bank of Rake combiners for processingsignal multipaths, said multi-user detector apparatus comprising: apartial cross-correlation unit (PCU) linked to the CEU for calculatingthe elements of a cross-correlation matrix related to the user codesequences; and a superconducting digital rapid single flux quantum(RSFQ) vector processing unit linked to the PCU and to the bank ofmatched filters, wherein the output vectors from the bank of matchedfilters are fed into the RSFQ vector processing unit for iterativeprocessing, and wherein the output of the RSFQ processing unit is fedinto the bank of Rake combiners for removing interference componentsfrom original user code sequences.
 20. An apparatus according to claim19 wherein the RSFQ vector processing unit is a vector machine thatcomprises superconducting logic circuits capable of performing asuccessive interference cancellation (SIC) iterative technique fordecorrelating the cross-correlation matrix associated with the user codesequences and is designated as an RSFQ SIC.
 21. An apparatus accordingto claim 20 wherein the RSFQ SIC comprises a plurality ofMultiply-accumulate units (MAC) such that all processors in one chainare performed in parallel in such a way that each block is computing onededicated element of vector of transmitted symbols at rime T.
 22. Anapparatus according to claim 20 wherein an iterative linear systemsolver (ILSS) for processing a Gauss-Seidel algorithm in said SICiterative technique.
 23. An apparatus according to claim 20 wherein theRSFQ SIC further comprises an iterative linear system solver (ILSS) unitfor running a Gauss-Seidel algorithm.
 24. A multi-user detectorapparatus for use with interference cancellation in a radio base stationreceiver operating within a direct sequence CDMA wireless system,wherein said radio base station receiver includes a channel estimationunit (CEU) for estimating characteristics of a channel, a pseudo-numbergenerator for use with despreading user code sequences, a bank ofmatched filters for rejecting interference in an input signal, a bank ofRake combiners for processing signal multipaths, said multi-userdetector apparatus comprising: a partial cross-correlation unit (PCU)linked to the CEU for calculating elements of a cross-correlation matrixrelated to the user code sequences; and a superconducting digital rapidsingle flux quantum (RSFQ) vector processing unit linked to the PCU andto the bank of matched filters, wherein the output vectors from the bankof matched filters are fed into the bank of Rake combiners, and theoutput vectors of the bank of Rake combiners are fed into the RSFQvector processing unit for iterative processing for removinginterference components from original user code sequences.
 25. Anapparatus according to claim 24 wherein the RSFQ vector processing unitis a vector machine that comprises superconducting logic circuitscapable of performing a successive interference cancellation (SIC)iterative technique for decorrelating the cross-correlation matrixassociated with the user code sequences and is designated as an RSFQSIC.
 26. An apparatus according to claim 25 wherein the RSFQ SICcomprises a plurality of Multiply-accumulate units (MAC) such that allprocessors in one chain are performed in parallel in such a way thateach block is computing one dedicated element of vector of transmittedsymbols at time T.
 27. An apparatus according to claim 25 wherein aniterative linear system solver (ILSS) for processing a Gauss-Seidelalgorithm in said SIC iterative technique.
 28. An apparatus according toclaim 25 wherein the RSFQ SIC further comprises an iterative linearsystem solver (ILSS) unit for running a Gauss-Seidel algorithm.
 29. Anapparatus according to claim 24 wherein signal processing required formulti-user detection is located before the bank of matched filters and amemory for storing signal processing data is included such that a sizeof the memory is in proportion to a total number of transmitted signalsper packet multiplied by an oversampling factor.
 30. A method ofreducing multi-user interference (MAI) in a wireless direct sequenceCDMA wireless system comprising at least one base station transmittingto and receiving signals from a plurality of mobile station users,wherein the method includes reducing interference with a multi-userdetector located within said at least one base station comprising thesteps of: receiving at the at least one base station a digital inputsignal comprised of a plurality of binary code sequences from the mobilestation users; processing the digital input signal with a bank ofmatched filters and producing matched filter output vectors from thebank of matched filters; feeding the matched filter output vectors intoone of a superconducting digital rapid single flux quantum (RSFQ) vectorprocessing unit and a bank of Rake combiners applying a successiveinterference cancellation (SIC) iterative technique (RSFQ SIC) to solvea cross-correlation matrix to decorrelate the binary code sequences;reducing the interference by removing interference components betweenthe binary code sequences; and feeding an output from the RSFQ vectorprocessing unit into the bank of Rake combiners to recover original datatransmitted by the mobile station users, or feeding the output of thebank of Rake combiners into the RSFQ vector processing unit.